Overview

Dataset statistics

Number of variables26
Number of observations8161
Missing cells2405
Missing cells (%)1.1%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory2.9 MiB
Average record size in memory368.3 B

Variable types

Numeric14
Categorical12

Alerts

age is highly overall correlated with home_kidsHigh correlation
home_kids is highly overall correlated with age and 1 other fieldsHigh correlation
income is highly overall correlated with home_valHigh correlation
home_val is highly overall correlated with income and 1 other fieldsHigh correlation
old_claim is highly overall correlated with clm_freqHigh correlation
clm_freq is highly overall correlated with old_claimHigh correlation
parent1 is highly overall correlated with home_kidsHigh correlation
m_status is highly overall correlated with home_valHigh correlation
sex is highly overall correlated with car_type and 1 other fieldsHigh correlation
education_level is highly overall correlated with jobHigh correlation
job is highly overall correlated with education_level and 1 other fieldsHigh correlation
commercial_car_use is highly overall correlated with job and 1 other fieldsHigh correlation
car_type is highly overall correlated with sex and 1 other fieldsHigh correlation
red_car is highly overall correlated with sexHigh correlation
kids_driv is highly imbalanced (70.9%)Imbalance
yoj has 454 (5.6%) missing valuesMissing
income has 445 (5.5%) missing valuesMissing
home_val has 464 (5.7%) missing valuesMissing
job has 526 (6.4%) missing valuesMissing
car_age has 510 (6.2%) missing valuesMissing
id is uniformly distributedUniform
id has unique valuesUnique
amt has 6008 (73.6%) zerosZeros
home_kids has 5289 (64.8%) zerosZeros
yoj has 625 (7.7%) zerosZeros
income has 615 (7.5%) zerosZeros
home_val has 2294 (28.1%) zerosZeros
old_claim has 5009 (61.4%) zerosZeros
clm_freq has 5009 (61.4%) zerosZeros
mvr_pts has 3712 (45.5%) zerosZeros

Reproduction

Analysis started2023-02-18 11:54:56.084977
Analysis finished2023-02-18 11:56:39.048290
Duration1 minute and 42.96 seconds
Software versionpandas-profiling v3.6.6
Download configurationconfig.json

Variables

id
Real number (ℝ)

UNIFORM  UNIQUE 

Distinct8161
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5151.8677
Minimum1
Maximum10302
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size63.9 KiB
2023-02-18T12:56:39.347023image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile509
Q12559
median5133
Q37745
95-th percentile9791
Maximum10302
Range10301
Interquartile range (IQR)5186

Descriptive statistics

Standard deviation2978.894
Coefficient of variation (CV)0.57821632
Kurtosis-1.2029827
Mean5151.8677
Median Absolute Deviation (MAD)2591
Skewness0.0020046137
Sum42044392
Variance8873809.2
MonotonicityStrictly increasing
2023-02-18T12:56:40.127905image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 1
 
< 0.1%
6874 1
 
< 0.1%
6890 1
 
< 0.1%
6889 1
 
< 0.1%
6888 1
 
< 0.1%
6887 1
 
< 0.1%
6886 1
 
< 0.1%
6884 1
 
< 0.1%
6883 1
 
< 0.1%
6882 1
 
< 0.1%
Other values (8151) 8151
99.9%
ValueCountFrequency (%)
1 1
< 0.1%
2 1
< 0.1%
4 1
< 0.1%
5 1
< 0.1%
6 1
< 0.1%
7 1
< 0.1%
8 1
< 0.1%
11 1
< 0.1%
12 1
< 0.1%
13 1
< 0.1%
ValueCountFrequency (%)
10302 1
< 0.1%
10301 1
< 0.1%
10299 1
< 0.1%
10298 1
< 0.1%
10297 1
< 0.1%
10296 1
< 0.1%
10295 1
< 0.1%
10293 1
< 0.1%
10292 1
< 0.1%
10291 1
< 0.1%

label
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size462.4 KiB
0
6008 
1
2153 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters8161
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 6008
73.6%
1 2153
 
26.4%

Length

2023-02-18T12:56:40.645429image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-02-18T12:56:41.176785image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
0 6008
73.6%
1 2153
 
26.4%

Most occurring characters

ValueCountFrequency (%)
0 6008
73.6%
1 2153
 
26.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 8161
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 6008
73.6%
1 2153
 
26.4%

Most occurring scripts

ValueCountFrequency (%)
Common 8161
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 6008
73.6%
1 2153
 
26.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 8161
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 6008
73.6%
1 2153
 
26.4%

amt
Real number (ℝ)

Distinct1949
Distinct (%)23.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1504.3246
Minimum0
Maximum107586.14
Zeros6008
Zeros (%)73.6%
Negative0
Negative (%)0.0%
Memory size63.9 KiB
2023-02-18T12:56:41.805876image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q31036
95-th percentile6452
Maximum107586.14
Range107586.14
Interquartile range (IQR)1036

Descriptive statistics

Standard deviation4704.0269
Coefficient of variation (CV)3.1270025
Kurtosis112.38628
Mean1504.3246
Median Absolute Deviation (MAD)0
Skewness8.7095047
Sum12276793
Variance22127869
MonotonicityNot monotonic
2023-02-18T12:56:42.795033image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 6008
73.6%
2327 4
 
< 0.1%
3350 3
 
< 0.1%
980 3
 
< 0.1%
3667 3
 
< 0.1%
2489 3
 
< 0.1%
2493 3
 
< 0.1%
5453 3
 
< 0.1%
3501 3
 
< 0.1%
5728 3
 
< 0.1%
Other values (1939) 2125
 
26.0%
ValueCountFrequency (%)
0 6008
73.6%
30.27728015 1
 
< 0.1%
58.53106231 1
 
< 0.1%
95.56731717 1
 
< 0.1%
108.7414986 1
 
< 0.1%
159.1509202 1
 
< 0.1%
196.1468185 1
 
< 0.1%
223.6120015 1
 
< 0.1%
262.0385439 1
 
< 0.1%
291.7285708 1
 
< 0.1%
ValueCountFrequency (%)
107586.1362 1
< 0.1%
85523.65335 1
< 0.1%
78874.19056 1
< 0.1%
77907.43028 1
< 0.1%
73783.46592 1
< 0.1%
64181.71033 1
< 0.1%
60846.53042 1
< 0.1%
60838.10394 1
< 0.1%
58851.06776 1
< 0.1%
56399.75387 1
< 0.1%

kids_driv
Categorical

Distinct5
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size462.4 KiB
0
7180 
1
 
636
2
 
279
3
 
62
4
 
4

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters8161
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 7180
88.0%
1 636
 
7.8%
2 279
 
3.4%
3 62
 
0.8%
4 4
 
< 0.1%

Length

2023-02-18T12:56:43.469751image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-02-18T12:56:44.057252image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
0 7180
88.0%
1 636
 
7.8%
2 279
 
3.4%
3 62
 
0.8%
4 4
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
0 7180
88.0%
1 636
 
7.8%
2 279
 
3.4%
3 62
 
0.8%
4 4
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 8161
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 7180
88.0%
1 636
 
7.8%
2 279
 
3.4%
3 62
 
0.8%
4 4
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
Common 8161
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 7180
88.0%
1 636
 
7.8%
2 279
 
3.4%
3 62
 
0.8%
4 4
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 8161
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 7180
88.0%
1 636
 
7.8%
2 279
 
3.4%
3 62
 
0.8%
4 4
 
< 0.1%

age
Real number (ℝ)

Distinct60
Distinct (%)0.7%
Missing6
Missing (%)0.1%
Infinite0
Infinite (%)0.0%
Mean44.790313
Minimum16
Maximum81
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size63.9 KiB
2023-02-18T12:56:44.613296image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum16
5-th percentile30
Q139
median45
Q351
95-th percentile59
Maximum81
Range65
Interquartile range (IQR)12

Descriptive statistics

Standard deviation8.6275895
Coefficient of variation (CV)0.19262177
Kurtosis-0.060282517
Mean44.790313
Median Absolute Deviation (MAD)6
Skewness-0.028999616
Sum365265
Variance74.4353
MonotonicityNot monotonic
2023-02-18T12:56:44.975429image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
46 401
 
4.9%
45 376
 
4.6%
48 363
 
4.4%
47 355
 
4.3%
43 351
 
4.3%
41 336
 
4.1%
44 336
 
4.1%
42 333
 
4.1%
50 329
 
4.0%
40 317
 
3.9%
Other values (50) 4658
57.1%
ValueCountFrequency (%)
16 5
 
0.1%
17 1
 
< 0.1%
18 3
 
< 0.1%
19 5
 
0.1%
20 3
 
< 0.1%
21 11
0.1%
22 14
0.2%
23 9
 
0.1%
24 21
0.3%
25 24
0.3%
ValueCountFrequency (%)
81 1
 
< 0.1%
80 1
 
< 0.1%
76 1
 
< 0.1%
73 3
 
< 0.1%
72 3
 
< 0.1%
70 6
 
0.1%
69 4
 
< 0.1%
68 8
 
0.1%
67 12
0.1%
66 24
0.3%

home_kids
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct6
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.72123514
Minimum0
Maximum5
Zeros5289
Zeros (%)64.8%
Negative0
Negative (%)0.0%
Memory size63.9 KiB
2023-02-18T12:56:45.342376image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q31
95-th percentile3
Maximum5
Range5
Interquartile range (IQR)1

Descriptive statistics

Standard deviation1.1163233
Coefficient of variation (CV)1.5477938
Kurtosis0.65101978
Mean0.72123514
Median Absolute Deviation (MAD)0
Skewness1.3416202
Sum5886
Variance1.2461777
MonotonicityNot monotonic
2023-02-18T12:56:45.622575image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
0 5289
64.8%
2 1118
 
13.7%
1 902
 
11.1%
3 674
 
8.3%
4 164
 
2.0%
5 14
 
0.2%
ValueCountFrequency (%)
0 5289
64.8%
1 902
 
11.1%
2 1118
 
13.7%
3 674
 
8.3%
4 164
 
2.0%
5 14
 
0.2%
ValueCountFrequency (%)
5 14
 
0.2%
4 164
 
2.0%
3 674
 
8.3%
2 1118
 
13.7%
1 902
 
11.1%
0 5289
64.8%

yoj
Real number (ℝ)

MISSING  ZEROS 

Distinct21
Distinct (%)0.3%
Missing454
Missing (%)5.6%
Infinite0
Infinite (%)0.0%
Mean10.499286
Minimum0
Maximum23
Zeros625
Zeros (%)7.7%
Negative0
Negative (%)0.0%
Memory size63.9 KiB
2023-02-18T12:56:45.947949image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q19
median11
Q313
95-th percentile15
Maximum23
Range23
Interquartile range (IQR)4

Descriptive statistics

Standard deviation4.0924742
Coefficient of variation (CV)0.38978594
Kurtosis1.179969
Mean10.499286
Median Absolute Deviation (MAD)2
Skewness-1.203436
Sum80918
Variance16.748345
MonotonicityNot monotonic
2023-02-18T12:56:46.191260image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=21)
ValueCountFrequency (%)
12 1158
14.2%
13 1016
12.4%
11 1003
12.3%
14 785
9.6%
10 749
9.2%
0 625
7.7%
9 521
6.4%
15 463
 
5.7%
8 384
 
4.7%
7 300
 
3.7%
Other values (11) 703
8.6%
(Missing) 454
 
5.6%
ValueCountFrequency (%)
0 625
7.7%
1 6
 
0.1%
2 15
 
0.2%
3 36
 
0.4%
4 37
 
0.5%
5 92
 
1.1%
6 173
 
2.1%
7 300
3.7%
8 384
4.7%
9 521
6.4%
ValueCountFrequency (%)
23 2
 
< 0.1%
19 12
 
0.1%
18 25
 
0.3%
17 101
 
1.2%
16 204
 
2.5%
15 463
 
5.7%
14 785
9.6%
13 1016
12.4%
12 1158
14.2%
11 1003
12.3%

income
Real number (ℝ)

HIGH CORRELATION  MISSING  ZEROS 

Distinct6612
Distinct (%)85.7%
Missing445
Missing (%)5.5%
Infinite0
Infinite (%)0.0%
Mean61898.095
Minimum0
Maximum367030
Zeros615
Zeros (%)7.5%
Negative0
Negative (%)0.0%
Memory size63.9 KiB
2023-02-18T12:56:46.520813image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q128097
median54028
Q385986
95-th percentile152274
Maximum367030
Range367030
Interquartile range (IQR)57889

Descriptive statistics

Standard deviation47572.683
Coefficient of variation (CV)0.76856458
Kurtosis2.1325051
Mean61898.095
Median Absolute Deviation (MAD)28188.5
Skewness1.186778
Sum4.776057 × 108
Variance2.2631601 × 109
MonotonicityNot monotonic
2023-02-18T12:56:46.837986image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 615
 
7.5%
61790 4
 
< 0.1%
26840 4
 
< 0.1%
48509 4
 
< 0.1%
158 3
 
< 0.1%
65885 3
 
< 0.1%
48741 3
 
< 0.1%
47513 3
 
< 0.1%
23157 3
 
< 0.1%
20887 3
 
< 0.1%
Other values (6602) 7071
86.6%
(Missing) 445
 
5.5%
ValueCountFrequency (%)
0 615
7.5%
5 1
 
< 0.1%
7 1
 
< 0.1%
18 1
 
< 0.1%
70 1
 
< 0.1%
77 1
 
< 0.1%
130 1
 
< 0.1%
138 1
 
< 0.1%
158 3
 
< 0.1%
195 1
 
< 0.1%
ValueCountFrequency (%)
367030 1
< 0.1%
332339 1
< 0.1%
320127 1
< 0.1%
309628 1
< 0.1%
306277 1
< 0.1%
297435 1
< 0.1%
290846 1
< 0.1%
284071 1
< 0.1%
282292 1
< 0.1%
282198 1
< 0.1%

parent1
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size462.4 KiB
0
7084 
1
1077 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters8161
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 7084
86.8%
1 1077
 
13.2%

Length

2023-02-18T12:56:47.295812image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-02-18T12:56:47.772380image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
0 7084
86.8%
1 1077
 
13.2%

Most occurring characters

ValueCountFrequency (%)
0 7084
86.8%
1 1077
 
13.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 8161
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 7084
86.8%
1 1077
 
13.2%

Most occurring scripts

ValueCountFrequency (%)
Common 8161
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 7084
86.8%
1 1077
 
13.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 8161
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 7084
86.8%
1 1077
 
13.2%

home_val
Real number (ℝ)

HIGH CORRELATION  MISSING  ZEROS 

Distinct5106
Distinct (%)66.3%
Missing464
Missing (%)5.7%
Infinite0
Infinite (%)0.0%
Mean154867.29
Minimum0
Maximum885282
Zeros2294
Zeros (%)28.1%
Negative0
Negative (%)0.0%
Memory size63.9 KiB
2023-02-18T12:56:48.364757image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median161160
Q3238724
95-th percentile374871
Maximum885282
Range885282
Interquartile range (IQR)238724

Descriptive statistics

Standard deviation129123.77
Coefficient of variation (CV)0.83377048
Kurtosis-0.014538371
Mean154867.29
Median Absolute Deviation (MAD)99735
Skewness0.48878547
Sum1.1920135 × 109
Variance1.6672949 × 1010
MonotonicityNot monotonic
2023-02-18T12:56:48.698792image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 2294
28.1%
115249 3
 
< 0.1%
288592 3
 
< 0.1%
173130 3
 
< 0.1%
153061 3
 
< 0.1%
159568 3
 
< 0.1%
123109 3
 
< 0.1%
332673 3
 
< 0.1%
238724 3
 
< 0.1%
111129 3
 
< 0.1%
Other values (5096) 5376
65.9%
(Missing) 464
 
5.7%
ValueCountFrequency (%)
0 2294
28.1%
50223 1
 
< 0.1%
50343 1
 
< 0.1%
50964 1
 
< 0.1%
51038 1
 
< 0.1%
51401 2
 
< 0.1%
51796 1
 
< 0.1%
51893 1
 
< 0.1%
52557 1
 
< 0.1%
52929 1
 
< 0.1%
ValueCountFrequency (%)
885282 1
< 0.1%
750455 1
< 0.1%
738153 1
< 0.1%
682634 1
< 0.1%
657804 1
< 0.1%
653952 1
< 0.1%
649247 1
< 0.1%
631309 1
< 0.1%
630267 1
< 0.1%
611328 1
< 0.1%

m_status
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size462.4 KiB
1
4894 
0
3267 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters8161
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
1 4894
60.0%
0 3267
40.0%

Length

2023-02-18T12:56:49.013479image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-02-18T12:56:49.445301image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
1 4894
60.0%
0 3267
40.0%

Most occurring characters

ValueCountFrequency (%)
1 4894
60.0%
0 3267
40.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 8161
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 4894
60.0%
0 3267
40.0%

Most occurring scripts

ValueCountFrequency (%)
Common 8161
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 4894
60.0%
0 3267
40.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 8161
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 4894
60.0%
0 3267
40.0%

sex
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size462.4 KiB
f
4375 
m
3786 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters8161
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowm
2nd rowm
3rd rowf
4th rowm
5th rowf

Common Values

ValueCountFrequency (%)
f 4375
53.6%
m 3786
46.4%

Length

2023-02-18T12:56:49.971018image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-02-18T12:56:50.445510image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
f 4375
53.6%
m 3786
46.4%

Most occurring characters

ValueCountFrequency (%)
f 4375
53.6%
m 3786
46.4%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 8161
100.0%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
f 4375
53.6%
m 3786
46.4%

Most occurring scripts

ValueCountFrequency (%)
Latin 8161
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
f 4375
53.6%
m 3786
46.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 8161
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
f 4375
53.6%
m 3786
46.4%

education_level
Categorical

Distinct5
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size462.4 KiB
1
2330 
2
2242 
3
1658 
0
1203 
4
728 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters8161
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row4
2nd row1
3rd row1
4th row0
5th row4

Common Values

ValueCountFrequency (%)
1 2330
28.6%
2 2242
27.5%
3 1658
20.3%
0 1203
14.7%
4 728
 
8.9%

Length

2023-02-18T12:56:50.818017image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-02-18T12:56:51.269958image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
1 2330
28.6%
2 2242
27.5%
3 1658
20.3%
0 1203
14.7%
4 728
 
8.9%

Most occurring characters

ValueCountFrequency (%)
1 2330
28.6%
2 2242
27.5%
3 1658
20.3%
0 1203
14.7%
4 728
 
8.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 8161
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 2330
28.6%
2 2242
27.5%
3 1658
20.3%
0 1203
14.7%
4 728
 
8.9%

Most occurring scripts

ValueCountFrequency (%)
Common 8161
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 2330
28.6%
2 2242
27.5%
3 1658
20.3%
0 1203
14.7%
4 728
 
8.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 8161
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 2330
28.6%
2 2242
27.5%
3 1658
20.3%
0 1203
14.7%
4 728
 
8.9%

job
Categorical

HIGH CORRELATION  MISSING 

Distinct8
Distinct (%)0.1%
Missing526
Missing (%)6.4%
Memory size508.4 KiB
blue collar
1825 
clerical
1271 
professional
1117 
manager
988 
lawyer
835 
Other values (3)
1599 

Length

Max length12
Median length10
Mean length8.9643746
Min length6

Characters and Unicode

Total characters68443
Distinct characters22
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowprofessional
2nd rowblue collar
3rd rowclerical
4th rowblue collar
5th rowdoctor

Common Values

ValueCountFrequency (%)
blue collar 1825
22.4%
clerical 1271
15.6%
professional 1117
13.7%
manager 988
12.1%
lawyer 835
10.2%
student 712
 
8.7%
home maker 641
 
7.9%
doctor 246
 
3.0%
(Missing) 526
 
6.4%

Length

2023-02-18T12:56:51.821745image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-02-18T12:56:52.429850image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
blue 1825
18.1%
collar 1825
18.1%
clerical 1271
12.6%
professional 1117
11.1%
manager 988
9.8%
lawyer 835
8.3%
student 712
 
7.0%
home 641
 
6.3%
maker 641
 
6.3%
doctor 246
 
2.4%

Most occurring characters

ValueCountFrequency (%)
l 9969
14.6%
e 8030
11.7%
a 7665
11.2%
r 6923
10.1%
o 5192
 
7.6%
c 4613
 
6.7%
s 2946
 
4.3%
n 2817
 
4.1%
u 2537
 
3.7%
2466
 
3.6%
Other values (12) 15285
22.3%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 65977
96.4%
Space Separator 2466
 
3.6%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
l 9969
15.1%
e 8030
12.2%
a 7665
11.6%
r 6923
10.5%
o 5192
7.9%
c 4613
 
7.0%
s 2946
 
4.5%
n 2817
 
4.3%
u 2537
 
3.8%
i 2388
 
3.6%
Other values (11) 12897
19.5%
Space Separator
ValueCountFrequency (%)
2466
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 65977
96.4%
Common 2466
 
3.6%

Most frequent character per script

Latin
ValueCountFrequency (%)
l 9969
15.1%
e 8030
12.2%
a 7665
11.6%
r 6923
10.5%
o 5192
7.9%
c 4613
 
7.0%
s 2946
 
4.5%
n 2817
 
4.3%
u 2537
 
3.8%
i 2388
 
3.6%
Other values (11) 12897
19.5%
Common
ValueCountFrequency (%)
2466
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 68443
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
l 9969
14.6%
e 8030
11.7%
a 7665
11.2%
r 6923
10.1%
o 5192
 
7.6%
c 4613
 
6.7%
s 2946
 
4.3%
n 2817
 
4.1%
u 2537
 
3.7%
2466
 
3.6%
Other values (12) 15285
22.3%

trav_time
Real number (ℝ)

Distinct97
Distinct (%)1.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean33.485725
Minimum5
Maximum142
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size63.9 KiB
2023-02-18T12:56:53.277808image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum5
5-th percentile7
Q122
median33
Q344
95-th percentile60
Maximum142
Range137
Interquartile range (IQR)22

Descriptive statistics

Standard deviation15.908333
Coefficient of variation (CV)0.47507807
Kurtosis0.66637462
Mean33.485725
Median Absolute Deviation (MAD)11
Skewness0.44698169
Sum273277
Variance253.07507
MonotonicityNot monotonic
2023-02-18T12:56:53.576473image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
5 334
 
4.1%
35 219
 
2.7%
30 219
 
2.7%
32 214
 
2.6%
25 214
 
2.6%
36 211
 
2.6%
29 207
 
2.5%
33 206
 
2.5%
24 204
 
2.5%
37 202
 
2.5%
Other values (87) 5931
72.7%
ValueCountFrequency (%)
5 334
4.1%
6 49
 
0.6%
7 43
 
0.5%
8 54
 
0.7%
9 70
 
0.9%
10 87
 
1.1%
11 71
 
0.9%
12 97
 
1.2%
13 97
 
1.2%
14 102
 
1.2%
ValueCountFrequency (%)
142 1
< 0.1%
134 1
< 0.1%
124 1
< 0.1%
113 1
< 0.1%
103 1
< 0.1%
101 1
< 0.1%
98 1
< 0.1%
97 2
< 0.1%
95 2
< 0.1%
93 1
< 0.1%
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size462.4 KiB
0
5132 
1
3029 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters8161
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row1
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 5132
62.9%
1 3029
37.1%

Length

2023-02-18T12:56:54.234635image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-02-18T12:56:54.826849image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
0 5132
62.9%
1 3029
37.1%

Most occurring characters

ValueCountFrequency (%)
0 5132
62.9%
1 3029
37.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 8161
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 5132
62.9%
1 3029
37.1%

Most occurring scripts

ValueCountFrequency (%)
Common 8161
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 5132
62.9%
1 3029
37.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 8161
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 5132
62.9%
1 3029
37.1%

blue_book
Real number (ℝ)

Distinct2789
Distinct (%)34.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean15709.9
Minimum1500
Maximum69740
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size63.9 KiB
2023-02-18T12:56:55.299439image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum1500
5-th percentile4900
Q19280
median14440
Q320850
95-th percentile31110
Maximum69740
Range68240
Interquartile range (IQR)11570

Descriptive statistics

Standard deviation8419.7341
Coefficient of variation (CV)0.53595085
Kurtosis0.79350644
Mean15709.9
Median Absolute Deviation (MAD)5700
Skewness0.79450615
Sum1.2820849 × 108
Variance70891922
MonotonicityNot monotonic
2023-02-18T12:56:55.780909image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1500 157
 
1.9%
6000 34
 
0.4%
6200 33
 
0.4%
5800 33
 
0.4%
6400 31
 
0.4%
5900 30
 
0.4%
6100 30
 
0.4%
6500 29
 
0.4%
5400 28
 
0.3%
5600 26
 
0.3%
Other values (2779) 7730
94.7%
ValueCountFrequency (%)
1500 157
1.9%
1520 1
 
< 0.1%
1530 1
 
< 0.1%
1540 1
 
< 0.1%
1590 1
 
< 0.1%
1620 1
 
< 0.1%
1650 1
 
< 0.1%
1670 2
 
< 0.1%
1680 1
 
< 0.1%
1700 1
 
< 0.1%
ValueCountFrequency (%)
69740 1
< 0.1%
65970 1
< 0.1%
62240 1
< 0.1%
61050 1
< 0.1%
57970 1
< 0.1%
50970 1
< 0.1%
50180 1
< 0.1%
49880 1
< 0.1%
49230 1
< 0.1%
48620 1
< 0.1%

tif
Real number (ℝ)

Distinct23
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.351305
Minimum1
Maximum25
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size63.9 KiB
2023-02-18T12:56:56.398724image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median4
Q37
95-th percentile13
Maximum25
Range24
Interquartile range (IQR)6

Descriptive statistics

Standard deviation4.1466353
Coefficient of variation (CV)0.77488301
Kurtosis0.42432793
Mean5.351305
Median Absolute Deviation (MAD)3
Skewness0.89113956
Sum43672
Variance17.194584
MonotonicityNot monotonic
2023-02-18T12:56:56.801462image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=23)
ValueCountFrequency (%)
1 2533
31.0%
6 1341
16.4%
4 1242
15.2%
10 780
 
9.6%
7 620
 
7.6%
3 424
 
5.2%
13 278
 
3.4%
11 242
 
3.0%
9 225
 
2.8%
17 104
 
1.3%
Other values (13) 372
 
4.6%
ValueCountFrequency (%)
1 2533
31.0%
2 6
 
0.1%
3 424
 
5.2%
4 1242
15.2%
5 52
 
0.6%
6 1341
16.4%
7 620
 
7.6%
8 60
 
0.7%
9 225
 
2.8%
10 780
 
9.6%
ValueCountFrequency (%)
25 2
 
< 0.1%
22 3
 
< 0.1%
21 11
 
0.1%
20 8
 
0.1%
19 8
 
0.1%
18 24
 
0.3%
17 104
1.3%
16 44
0.5%
15 31
 
0.4%
14 78
1.0%

car_type
Categorical

Distinct6
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size502.2 KiB
suv
2294 
minivan
2145 
pickup
1389 
sports car
907 
van
750 

Length

Max length11
Median length10
Mean length6.0025732
Min length3

Characters and Unicode

Total characters48987
Distinct characters16
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowminivan
2nd rowminivan
3rd rowsuv
4th rowminivan
5th rowsuv

Common Values

ValueCountFrequency (%)
suv 2294
28.1%
minivan 2145
26.3%
pickup 1389
17.0%
sports car 907
 
11.1%
van 750
 
9.2%
panel truck 676
 
8.3%

Length

2023-02-18T12:56:57.461615image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-02-18T12:56:58.454107image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
suv 2294
23.5%
minivan 2145
22.0%
pickup 1389
14.3%
sports 907
 
9.3%
car 907
 
9.3%
van 750
 
7.7%
panel 676
 
6.9%
truck 676
 
6.9%

Most occurring characters

ValueCountFrequency (%)
n 5716
11.7%
i 5679
11.6%
v 5189
10.6%
a 4478
9.1%
p 4361
8.9%
u 4359
8.9%
s 4108
8.4%
c 2972
 
6.1%
r 2490
 
5.1%
m 2145
 
4.4%
Other values (6) 7490
15.3%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 47404
96.8%
Space Separator 1583
 
3.2%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
n 5716
12.1%
i 5679
12.0%
v 5189
10.9%
a 4478
9.4%
p 4361
9.2%
u 4359
9.2%
s 4108
8.7%
c 2972
6.3%
r 2490
5.3%
m 2145
 
4.5%
Other values (5) 5907
12.5%
Space Separator
ValueCountFrequency (%)
1583
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 47404
96.8%
Common 1583
 
3.2%

Most frequent character per script

Latin
ValueCountFrequency (%)
n 5716
12.1%
i 5679
12.0%
v 5189
10.9%
a 4478
9.4%
p 4361
9.2%
u 4359
9.2%
s 4108
8.7%
c 2972
6.3%
r 2490
5.3%
m 2145
 
4.5%
Other values (5) 5907
12.5%
Common
ValueCountFrequency (%)
1583
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 48987
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
n 5716
11.7%
i 5679
11.6%
v 5189
10.6%
a 4478
9.1%
p 4361
8.9%
u 4359
8.9%
s 4108
8.4%
c 2972
 
6.1%
r 2490
 
5.1%
m 2145
 
4.4%
Other values (6) 7490
15.3%

red_car
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size462.4 KiB
0
5783 
1
2378 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters8161
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row0
4th row1
5th row0

Common Values

ValueCountFrequency (%)
0 5783
70.9%
1 2378
29.1%

Length

2023-02-18T12:56:59.348565image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-02-18T12:57:00.030671image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
0 5783
70.9%
1 2378
29.1%

Most occurring characters

ValueCountFrequency (%)
0 5783
70.9%
1 2378
29.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 8161
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 5783
70.9%
1 2378
29.1%

Most occurring scripts

ValueCountFrequency (%)
Common 8161
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 5783
70.9%
1 2378
29.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 8161
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 5783
70.9%
1 2378
29.1%

old_claim
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct2857
Distinct (%)35.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4037.0762
Minimum0
Maximum57037
Zeros5009
Zeros (%)61.4%
Negative0
Negative (%)0.0%
Memory size63.9 KiB
2023-02-18T12:57:00.443835image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q34636
95-th percentile27090
Maximum57037
Range57037
Interquartile range (IQR)4636

Descriptive statistics

Standard deviation8777.1391
Coefficient of variation (CV)2.1741326
Kurtosis9.8705921
Mean4037.0762
Median Absolute Deviation (MAD)0
Skewness3.1201869
Sum32946579
Variance77038171
MonotonicityNot monotonic
2023-02-18T12:57:00.896818image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 5009
61.4%
4263 4
 
< 0.1%
1391 4
 
< 0.1%
1310 4
 
< 0.1%
4538 3
 
< 0.1%
6281 3
 
< 0.1%
5289 3
 
< 0.1%
3863 3
 
< 0.1%
1552 3
 
< 0.1%
1994 3
 
< 0.1%
Other values (2847) 3122
38.3%
ValueCountFrequency (%)
0 5009
61.4%
502 1
 
< 0.1%
506 1
 
< 0.1%
518 1
 
< 0.1%
519 1
 
< 0.1%
521 1
 
< 0.1%
536 1
 
< 0.1%
537 1
 
< 0.1%
548 1
 
< 0.1%
551 1
 
< 0.1%
ValueCountFrequency (%)
57037 1
< 0.1%
53986 1
< 0.1%
53568 1
< 0.1%
53477 1
< 0.1%
52507 1
< 0.1%
52465 1
< 0.1%
52445 1
< 0.1%
52068 1
< 0.1%
51904 1
< 0.1%
51593 1
< 0.1%

clm_freq
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct6
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.7985541
Minimum0
Maximum5
Zeros5009
Zeros (%)61.4%
Negative0
Negative (%)0.0%
Memory size63.9 KiB
2023-02-18T12:57:01.294216image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q32
95-th percentile3
Maximum5
Range5
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.1584527
Coefficient of variation (CV)1.4506878
Kurtosis0.2860043
Mean0.7985541
Median Absolute Deviation (MAD)0
Skewness1.209243
Sum6517
Variance1.3420126
MonotonicityNot monotonic
2023-02-18T12:57:01.544132image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
0 5009
61.4%
2 1171
 
14.3%
1 997
 
12.2%
3 776
 
9.5%
4 190
 
2.3%
5 18
 
0.2%
ValueCountFrequency (%)
0 5009
61.4%
1 997
 
12.2%
2 1171
 
14.3%
3 776
 
9.5%
4 190
 
2.3%
5 18
 
0.2%
ValueCountFrequency (%)
5 18
 
0.2%
4 190
 
2.3%
3 776
 
9.5%
2 1171
 
14.3%
1 997
 
12.2%
0 5009
61.4%

revoced
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size462.4 KiB
0
7161 
1
1000 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters8161
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row1

Common Values

ValueCountFrequency (%)
0 7161
87.7%
1 1000
 
12.3%

Length

2023-02-18T12:57:01.904287image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-02-18T12:57:02.327606image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
0 7161
87.7%
1 1000
 
12.3%

Most occurring characters

ValueCountFrequency (%)
0 7161
87.7%
1 1000
 
12.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 8161
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 7161
87.7%
1 1000
 
12.3%

Most occurring scripts

ValueCountFrequency (%)
Common 8161
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 7161
87.7%
1 1000
 
12.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 8161
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 7161
87.7%
1 1000
 
12.3%

mvr_pts
Real number (ℝ)

Distinct13
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.695503
Minimum0
Maximum13
Zeros3712
Zeros (%)45.5%
Negative0
Negative (%)0.0%
Memory size63.9 KiB
2023-02-18T12:57:02.671579image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1
Q33
95-th percentile6
Maximum13
Range13
Interquartile range (IQR)3

Descriptive statistics

Standard deviation2.1471117
Coefficient of variation (CV)1.2663568
Kurtosis1.3781418
Mean1.695503
Median Absolute Deviation (MAD)1
Skewness1.3483359
Sum13837
Variance4.6100888
MonotonicityNot monotonic
2023-02-18T12:57:03.053641image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=13)
ValueCountFrequency (%)
0 3712
45.5%
1 1157
 
14.2%
2 948
 
11.6%
3 758
 
9.3%
4 599
 
7.3%
5 399
 
4.9%
6 266
 
3.3%
7 167
 
2.0%
8 84
 
1.0%
9 45
 
0.6%
Other values (3) 26
 
0.3%
ValueCountFrequency (%)
0 3712
45.5%
1 1157
 
14.2%
2 948
 
11.6%
3 758
 
9.3%
4 599
 
7.3%
5 399
 
4.9%
6 266
 
3.3%
7 167
 
2.0%
8 84
 
1.0%
9 45
 
0.6%
ValueCountFrequency (%)
13 2
 
< 0.1%
11 11
 
0.1%
10 13
 
0.2%
9 45
 
0.6%
8 84
 
1.0%
7 167
 
2.0%
6 266
 
3.3%
5 399
4.9%
4 599
7.3%
3 758
9.3%

car_age
Real number (ℝ)

Distinct30
Distinct (%)0.4%
Missing510
Missing (%)6.2%
Infinite0
Infinite (%)0.0%
Mean8.3283231
Minimum-3
Maximum28
Zeros3
Zeros (%)< 0.1%
Negative1
Negative (%)< 0.1%
Memory size63.9 KiB
2023-02-18T12:57:03.406052image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum-3
5-th percentile1
Q11
median8
Q312
95-th percentile18
Maximum28
Range31
Interquartile range (IQR)11

Descriptive statistics

Standard deviation5.7007424
Coefficient of variation (CV)0.68450063
Kurtosis-0.74809176
Mean8.3283231
Median Absolute Deviation (MAD)5
Skewness0.28206372
Sum63720
Variance32.498464
MonotonicityNot monotonic
2023-02-18T12:57:03.661487image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=30)
ValueCountFrequency (%)
1 1934
23.7%
8 537
 
6.6%
9 526
 
6.4%
7 524
 
6.4%
10 469
 
5.7%
11 460
 
5.6%
6 451
 
5.5%
12 368
 
4.5%
13 356
 
4.4%
14 311
 
3.8%
Other values (20) 1715
21.0%
(Missing) 510
 
6.2%
ValueCountFrequency (%)
-3 1
 
< 0.1%
0 3
 
< 0.1%
1 1934
23.7%
2 12
 
0.1%
3 54
 
0.7%
4 135
 
1.7%
5 305
 
3.7%
6 451
 
5.5%
7 524
 
6.4%
8 537
 
6.6%
ValueCountFrequency (%)
28 1
 
< 0.1%
27 1
 
< 0.1%
26 2
 
< 0.1%
25 6
 
0.1%
24 10
 
0.1%
23 18
 
0.2%
22 27
 
0.3%
21 51
 
0.6%
20 90
1.1%
19 128
1.6%

urban_city
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size462.4 KiB
1
6492 
0
1669 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters8161
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
1 6492
79.5%
0 1669
 
20.5%

Length

2023-02-18T12:57:03.950464image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-02-18T12:57:04.378810image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
1 6492
79.5%
0 1669
 
20.5%

Most occurring characters

ValueCountFrequency (%)
1 6492
79.5%
0 1669
 
20.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 8161
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 6492
79.5%
0 1669
 
20.5%

Most occurring scripts

ValueCountFrequency (%)
Common 8161
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 6492
79.5%
0 1669
 
20.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 8161
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 6492
79.5%
0 1669
 
20.5%

Interactions

2023-02-18T12:56:30.262727image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-18T12:55:01.902826image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-18T12:55:08.188504image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-18T12:55:15.040222image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-18T12:55:21.540854image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-18T12:55:26.787682image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-18T12:55:36.239684image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-18T12:55:43.314708image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-18T12:55:49.771080image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-18T12:55:56.915038image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-18T12:56:02.865611image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-18T12:56:10.860616image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-18T12:56:17.273766image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-18T12:56:23.788665image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-18T12:56:30.596014image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-18T12:55:02.306142image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-18T12:55:08.583783image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-18T12:55:15.488364image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-18T12:55:21.961734image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-18T12:55:27.151912image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-18T12:55:36.759197image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-18T12:55:43.680085image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-18T12:55:50.341870image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-18T12:55:57.297850image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-18T12:56:03.256120image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-18T12:56:11.685632image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-18T12:56:17.595138image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-18T12:56:24.497078image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-18T12:56:30.916162image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-18T12:55:02.799305image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-18T12:55:08.972919image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-18T12:55:15.913653image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-18T12:55:22.326508image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-18T12:55:27.915424image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-18T12:55:37.302592image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-18T12:55:44.052705image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-18T12:55:50.913877image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-18T12:55:57.704116image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-18T12:56:03.674776image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-18T12:56:12.321715image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-18T12:56:18.033756image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-18T12:56:25.059509image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-18T12:56:31.218991image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-18T12:55:03.275553image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-18T12:55:09.465244image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-18T12:55:16.327762image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-18T12:55:22.690903image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-18T12:55:28.555352image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-18T12:55:37.941263image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-18T12:55:44.520385image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-18T12:55:51.401808image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-18T12:55:58.134106image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-18T12:56:04.016966image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-18T12:56:12.741418image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-18T12:56:18.351259image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-18T12:56:25.555974image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-18T12:56:31.520002image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-18T12:55:03.794414image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-18T12:55:09.830771image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-18T12:55:16.750891image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-18T12:55:23.249974image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-18T12:55:29.178531image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-18T12:55:38.453752image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-18T12:55:44.947758image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-18T12:55:51.913079image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-18T12:55:58.541136image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-18T12:56:04.422612image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-18T12:56:13.246766image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-18T12:56:18.671193image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-18T12:56:26.052056image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-18T12:56:31.887764image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-18T12:55:04.167398image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-18T12:55:10.230679image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-18T12:55:17.276255image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-18T12:55:23.527411image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-18T12:55:29.716439image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-18T12:55:38.934083image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-18T12:55:45.315122image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-18T12:55:52.468411image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-18T12:55:58.880658image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-18T12:56:04.833029image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-18T12:56:13.654559image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-18T12:56:19.105941image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-18T12:56:26.701084image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-18T12:56:32.294430image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-18T12:55:04.621435image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-18T12:55:10.653727image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-18T12:55:17.931776image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-18T12:55:23.896206image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-18T12:55:30.529225image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-18T12:55:39.535310image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-18T12:55:45.786104image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-18T12:55:53.355806image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-18T12:55:59.486107image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-18T12:56:05.509892image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-18T12:56:14.129615image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-18T12:56:19.720383image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-18T12:56:27.273594image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-18T12:56:32.629865image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-18T12:55:05.002222image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-18T12:55:11.026988image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-18T12:55:18.447987image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-18T12:55:24.275078image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-18T12:55:31.453428image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-18T12:55:40.003698image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-18T12:55:46.162071image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-18T12:55:53.909437image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-18T12:56:00.026416image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-18T12:56:06.138912image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-18T12:56:14.611913image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-18T12:56:20.176939image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-18T12:56:27.732782image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-18T12:56:33.022093image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-18T12:55:05.466672image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-18T12:55:12.086681image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-18T12:55:18.855826image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-18T12:55:24.722178image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-18T12:55:32.148498image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-18T12:55:40.461658image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-18T12:55:46.594534image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-18T12:55:54.359220image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-18T12:56:00.471769image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-18T12:56:06.801336image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-18T12:56:14.999748image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-18T12:56:20.614589image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-18T12:56:28.202502image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-18T12:56:33.391196image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-18T12:55:05.961387image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-18T12:55:12.668910image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-18T12:55:19.456053image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-18T12:55:25.107653image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-18T12:55:32.799960image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-18T12:55:41.027337image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-18T12:55:47.060407image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-18T12:55:54.962587image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-18T12:56:00.918099image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-18T12:56:07.446539image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-18T12:56:15.402769image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-18T12:56:21.433305image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-18T12:56:28.645913image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-18T12:56:33.653005image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-18T12:55:06.369916image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-18T12:55:13.092616image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-18T12:55:19.948731image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-18T12:55:25.435036image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-18T12:55:33.347319image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-18T12:55:41.397126image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-18T12:55:47.473943image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-18T12:55:55.381702image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-18T12:56:01.313116image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-18T12:56:08.102897image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-18T12:56:15.774382image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-18T12:56:21.848785image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-18T12:56:28.968556image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-18T12:56:33.967682image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-18T12:55:06.908333image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-18T12:55:13.492701image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-18T12:55:20.470153image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-18T12:55:25.836143image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-18T12:55:34.153272image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-18T12:55:41.804871image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-18T12:55:48.119873image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-18T12:55:55.786069image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-18T12:56:01.697666image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-18T12:56:08.680836image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-18T12:56:16.180607image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-18T12:56:22.299149image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-18T12:56:29.343229image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-18T12:56:34.288525image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-18T12:55:07.302577image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-18T12:55:13.874924image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-18T12:55:20.825721image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-18T12:55:26.126520image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-18T12:55:34.933782image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-18T12:55:42.329287image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-18T12:55:48.614250image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-18T12:55:56.220048image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-18T12:56:02.047467image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-18T12:56:09.192852image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-18T12:56:16.603852image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-18T12:56:22.744857image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-18T12:56:29.654170image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-18T12:56:34.702979image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-18T12:55:07.776976image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-18T12:55:14.469406image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-18T12:55:21.208135image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-18T12:55:26.472476image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-18T12:55:35.738580image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-18T12:55:42.794866image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-18T12:55:49.173252image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-18T12:55:56.570742image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-18T12:56:02.434922image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-18T12:56:10.000956image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-18T12:56:16.951978image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-18T12:56:23.299007image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-18T12:56:29.947321image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Correlations

2023-02-18T12:57:04.754011image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
idamtagehome_kidsyojincomehome_valtrav_timeblue_booktifold_claimclm_freqmvr_ptscar_agelabelkids_drivparent1m_statussexeducation_leveljobcommercial_car_usecar_typered_carrevocedurban_city
id1.000-0.0020.037-0.0090.0270.0080.011-0.0250.014-0.0120.0100.0160.0080.0000.0000.0090.0000.0000.0180.0120.0110.0070.0110.0060.0000.000
amt-0.0021.000-0.1020.126-0.055-0.148-0.1850.055-0.106-0.0840.2300.2320.195-0.0990.2250.0090.0750.0560.0000.0170.0160.0430.0240.0060.0320.044
age0.037-0.1021.000-0.5160.1440.1820.2040.0070.165-0.002-0.038-0.034-0.0630.1790.1570.1650.3260.0950.0770.1230.1070.0610.0870.0760.0320.062
home_kids-0.0090.126-0.5161.0000.144-0.170-0.115-0.013-0.1160.0050.0520.0460.055-0.1600.1270.3220.5300.0390.1300.1050.1070.0000.0510.0810.0470.061
yoj0.027-0.0550.1440.1441.0000.2120.237-0.0050.0950.016-0.010-0.018-0.0270.0390.0900.0790.0750.2520.1180.0630.2390.0620.0720.0720.0080.104
income0.008-0.1480.182-0.1700.2121.0000.565-0.0400.4090.004-0.060-0.054-0.0470.4300.1470.0030.0730.0240.1030.3820.3120.1190.1350.0670.0000.213
home_val0.011-0.1850.204-0.1150.2370.5651.000-0.0300.2400.003-0.105-0.103-0.0610.2210.1860.0000.2980.5590.0760.2900.2610.0780.1110.0450.0380.143
trav_time-0.0250.0550.007-0.013-0.005-0.040-0.0301.000-0.011-0.008-0.0010.0080.009-0.0360.0580.0290.0320.0250.0000.0310.0420.0000.0000.0000.0070.173
blue_book0.014-0.1060.165-0.1160.0950.4090.240-0.0111.0000.001-0.040-0.042-0.0360.1930.1120.0000.0440.0000.0700.1500.1210.2470.3510.0390.0230.087
tif-0.012-0.084-0.0020.0050.0160.0040.003-0.0080.0011.000-0.028-0.024-0.042-0.0010.0810.0000.0250.0000.0060.0000.0120.0000.0070.0270.0160.026
old_claim0.0100.230-0.0380.052-0.010-0.060-0.105-0.001-0.040-0.0281.0000.9270.415-0.0220.1590.0180.0540.0440.0240.0000.0110.0360.0310.0000.4940.163
clm_freq0.0160.232-0.0340.046-0.018-0.054-0.1030.008-0.042-0.0240.9271.0000.414-0.0160.2410.0180.0570.0690.0000.0250.0260.0840.0350.0200.0720.271
mvr_pts0.0080.195-0.0630.055-0.027-0.047-0.0610.009-0.036-0.0420.4150.4141.000-0.0080.2220.0320.0690.0430.0150.0080.0220.0680.0290.0000.0610.148
car_age0.000-0.0990.179-0.1600.0390.4300.221-0.0360.193-0.001-0.022-0.016-0.0081.0000.1040.0220.0630.0280.0160.4220.2330.0920.0560.0200.0180.177
label0.0000.2250.1570.1270.0900.1470.1860.0580.1120.0810.1590.2410.2220.1041.0000.1040.1570.1340.0180.1430.1820.1420.1420.0000.1510.224
kids_driv0.0090.0090.1650.3220.0790.0030.0000.0290.0000.0000.0180.0180.0320.0220.1041.0000.2270.0390.0550.0320.0440.0050.0160.0500.0450.040
parent10.0000.0750.3260.5300.0750.0730.2980.0320.0440.0250.0540.0570.0690.0630.1570.2271.0000.4770.0730.0910.0900.0000.0560.0400.0480.019
m_status0.0000.0560.0950.0390.2520.0240.5590.0250.0000.0000.0440.0690.0430.0280.1340.0390.4771.0000.0000.0510.0350.0170.0000.0150.0410.000
sex0.0180.0000.0770.1300.1180.1030.0760.0000.0700.0060.0240.0000.0150.0160.0180.0550.0730.0001.0000.0430.2480.2790.7130.6660.0000.052
education_level0.0120.0170.1230.1050.0630.3820.2900.0310.1500.0000.0000.0250.0080.4220.1430.0320.0910.0510.0431.0000.5620.2210.0940.0250.0170.234
job0.0110.0160.1070.1070.2390.3120.2610.0420.1210.0120.0110.0260.0220.2330.1820.0440.0900.0350.2480.5621.0000.5770.1340.1770.0280.310
commercial_car_use0.0070.0430.0610.0000.0620.1190.0780.0000.2470.0000.0360.0840.0680.0920.1420.0050.0000.0170.2790.2210.5771.0000.5380.1890.0120.017
car_type0.0110.0240.0870.0510.0720.1350.1110.0000.3510.0070.0310.0350.0290.0560.1420.0160.0560.0000.7130.0940.1340.5381.0000.4840.0260.074
red_car0.0060.0060.0760.0810.0720.0670.0450.0000.0390.0270.0000.0200.0000.0200.0000.0500.0400.0150.6660.0250.1770.1890.4841.0000.0000.045
revoced0.0000.0320.0320.0470.0080.0000.0380.0070.0230.0160.4940.0720.0610.0180.1510.0450.0480.0410.0000.0170.0280.0120.0260.0001.0000.085
urban_city0.0000.0440.0620.0610.1040.2130.1430.1730.0870.0260.1630.2710.1480.1770.2240.0400.0190.0000.0520.2340.3100.0170.0740.0450.0851.000

Missing values

2023-02-18T12:56:35.222952image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
A simple visualization of nullity by column.
2023-02-18T12:56:37.084795image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2023-02-18T12:56:38.559158image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

idlabelamtkids_drivagehome_kidsyojincomeparent1home_valm_statussexeducation_leveljobtrav_timecommercial_car_useblue_booktifcar_typered_carold_claimclm_freqrevocedmvr_ptscar_ageurban_city
0100.0060.0011.067349.000.00m4professional14014230.011minivan14461.020318.01
1200.0043.0011.091449.00257252.00m1blue collar22114940.01minivan10.00001.01
2400.0035.0110.016039.00124191.01f1clerical504010.04suv038690.020310.01
3500.0051.0014.0NaN0306251.01m0blue collar32015440.07minivan10.00006.01
4600.0050.00NaN114986.00243925.01f4doctor36018000.01suv019217.021317.01
5712946.0034.0112.0125301.010.00f2blue collar46117430.01sports car00.00007.01
6800.0054.00NaN18755.00NaN1f0blue collar3308780.01suv00.00001.01
71114021.0137.02NaN107961.00333680.01m2blue collar44116970.01van12374.011107.01
81212501.0034.0010.062978.000.00f2clerical34011200.01suv00.00001.01
91300.0050.007.0106952.000.00m2professional48118510.07van00.000117.00
idlabelamtkids_drivagehome_kidsyojincomeparent1home_valm_statussexeducation_leveljobtrav_timecommercial_car_useblue_booktifcar_typered_carold_claimclm_freqrevocedmvr_ptscar_ageurban_city
81511029100.0054.0013.081818.00272725.01m2manager18119660.01van024690.01164.01
81521029200.0146.0012.045018.000.00m1blue collar26015060.04minivan033026.03001.00
81531029300.0048.0010.0111305.000.00f4doctor59017430.013suv00.000418.01
81541029500.0138.0416.012717.000.01f2student15124740.01pickup09245.030315.01
81551029600.0041.007.06256.000.00m1student4105600.01pickup00.00007.00
81561029700.0035.0011.043112.000.00m1blue collar51127330.010panel truck10.00008.00
81571029800.0145.029.0164669.00386273.01m4manager21013270.015minivan00.000217.01
81581029900.0046.009.0107204.00332591.01m3None36124490.06panel truck00.00001.01
81591030100.0050.007.043445.00149248.01f2home maker36022550.06minivan00.000011.01
81601030200.0052.0011.053235.00197017.01f1clerical64019400.06minivan00.00009.00